Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction

Abstract: We propose a parsimonious metric – the Adjusted Benford score (AB-score) – to improve the detection of financial misstatements. Based on Benford’s Law, which predicts the leading-digit distribution of naturally occurring numbers, the AB-score estimates a firm-year’s likelihood of financial statement manipulation, compared to its peers and controlling for time-series trends. The AB-score requires less data than the leading accounting-based misstatement metric (the F-score) and can be computed for many more firm-years, including for financial firms. For firm-years with all data available, combining the AB-score and F-score variables into one model yields higher accuracy in predicting misstatements in- and out-of-sample.